(2024) 22:294
Pérez‑Prieto et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03503-y

BMC Medicine

RESEARCH ARTICLE

Open Access

Gut microbiome in endometriosis: a cohort
study on 1000 individuals
Inmaculada Pérez‑Prieto1,2* , Eva Vargas1,2,3, Eduardo Salas‑Espejo1, Kreete Lüll4, Analuce Canha‑Gouveia1,2,5,
Laura Antequera Pérez1,6, Juan Fontes2,7, Andres Salumets8,9,10,11, Reidar Andreson4,12, Oliver Aasmets4, Estonian
Biobank research team4, Katrine Whiteson13, Elin Org4 and Signe Altmäe1,2,9,10

Abstract
Background Endometriosis, defined as the presence of endometrial-like tissue outside of the uterus, is oneof the most prevalent gynecological disorders. Although different theories have been proposed, its pathogenesis
is not clear. Novel studies indicate that the gut microbiome may be involved in the etiology of endometriosis; never‑theless, the connection between microbes, their dysbiosis, and the development of endometriosis is understudied.
This case–control study analyzed the gut microbiome in women with and without endometriosis to identify microbial
targets involved in the disease.
Methods A subsample of 1000 women from the Estonian Microbiome cohort, including 136 women with endo‑
metriosis and 864 control women, was analyzed. Microbial composition was determined by shotgun metagenomics
and microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG)
database. Partitioning Around Medoids (PAM) algorithm was performed to cluster the microbial profile of the Esto‑
nian population. The alpha- and beta-diversity and differential abundance analyses were performed to assess the gut
microbiome (species and KEGG orthologies (KO)) in both groups. Metagenomic reads were mapped to estrobolomerelated enzymes’ sequences to study potential microbiome-estrogen metabolism axis alterations in endometriosis.
Results Diversity analyses did not detect significant differences between women with and without endometriosis(alpha-diversity: all p-values > 0.05; beta-diversity: PERMANOVA, both R2 < 0.0007, p-values > 0.05). No differential spe‑
cies or pathways were detected after multiple testing adjustment (all FDR p-values > 0.05). Sensitivity analysis exclud‑ing women at menopause (> 50 years) confirmed our results. Estrobolome-associated enzymes’ sequence reads were
not significantly different between groups (all FDR p-values > 0.05).
Conclusions Our findings do not provide enough evidence to support the existence of a gut microbiomedependent mechanism directly implicated in the pathogenesis of endometriosis. To the best of our knowledge, thisis the largest metagenome study on endometriosis conducted to date.
Keywords Endometriosis, Estrobolome, Gut microbiota, Metagenomics, Microbiome, Microbiota, Shotgun
sequencing*Correspondence:
Inmaculada Pérez‑Prieto
inmapprieto@ugr.es
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Pérez‑Prieto et al. BMC Medicine(2024) 22:294

Background
Endometriosis, defined as the growth of endometrial-liketissue outside of the uterine cavity, is a common gynecologic disease, affecting approximately 5–10% of reproductive-aged women [1]. Endometrial lesions cause a chronic
inflammatory condition associated with a wide rangeof reported symptoms, including dysmenorrhea, pelvic
pain, dyspareunia, and infertility [2, 3]. Because these
symptoms are associated with other conditions, diagnosing endometriosis requires laparoscopic examination
with excisional biopsy for definitive pathology confirmation, which leads to a long diagnostic delay or frequentmisdiagnosis. Although endometriosis is a widespreadand burdening reproductive disorder, it has been historically understudied. Notably, proposed hypotheses suchas retrograde menstruation, coelomic metaplasia, andMüllerian remnants do not explain the etiology of all thedifferent phenotypes of endometriosis (i.e., superficial,ovarian and deep infiltrating endometriosis) [4]. Thus,
endometriosis represents an important public healthconcern with substantial effects on the quality of life of
millions of women globally [5, 6].
The microbiome refers to the collection of genomesof the microorganisms (bacteria, viruses, fungi, protozoa, and archaea) that inhabit a particular environment[7]. Particularly, the human gastrointestinal system is themost diverse microbiome within the human body, beingcolonized by trillions of microbes that play key roles inregulating host physiological functions [8, 9]. Indeed, ahealthy balanced gut microbiome is crucial for nutrient
absorption, gut epithelial barrier integrity, immune system work, and other body functions [10, 11]. Nevertheless, compositional and functional perturbations in the
microbiome could lead to an unstable state called dysbiosis, which is linked to different chronic conditions suchas obesity, type-2 diabetes, cancer, inflammatory bowel
diseases, and neurological and reproductive diseases,
among others [12–16].
Extensive research associates the gut microbiome withcirculating levels of estrogens through the secretion of
β-glucuronidase, an enzyme that deconjugates estrogen
into its active metabolized form [17]. The estrobolome
term encapsulates the gut gene repertoire of microbial
origin capable of metabolizing estrogens leading to thestimulation of epithelial proliferation throughout thefemale reproductive tract. Therefore, estrogen dysregulation has been shown to drive proliferative diseases suchas endometriosis along with its main comorbidities like
infertility and pelvic pain [18]. Indeed, the use of estrogen-progestins and progestins is the first-line medical
treatment of endometriosis due to their safety, tolerability, and favorable cost profile, although they are often

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ineffective and may lead to unwanted side effects [19].
Hence, to date, there is no cure for endometriosis andnew non-hormonal therapeutic approaches are becoming increasingly necessary [20].
Considering the influence of the gut microbiome on
immunomodulation and estrogen metabolism, alongside
the estrogen-driven inflammatory state in endometriosis, a potential role of the gut microbiome in the pathogenesis of the disease has been proposed [18, 21]. Recent
studies suggest that gut dysbiosis induces an incrementin the estrogen circulating levels, which may contributeto the hyper-estrogenic environment that promotes the
progression of endometriosis [22]. Nevertheless, the connection between microbes, their dysbiosis, and the development of endometriosis remains unexplored. Researchon the gut microbiome in endometriosis would enable
the identification of novel biomarkers for noninvasive
diagnostic and therapeutic approaches to identify andtreat women with endometriosis earlier [23].
This study aimed to analyze and compare the gut
microbiome profiles in a large cohort of women with andwithout endometriosis, to identify microbial signaturesand pathways potentially associated with the development of the disease. We also explored the link between
the estrogen metabolism and endometriosis by analyzing microbial enzyme reads of the estrobolome between
women with endometriosis and controls.

Methods
Study population

This case–control study included 1000 women of the Estonian Microbiome (EstMB) cohort (age = 45.61 ± 10.36 years;
BMI = 25.67 ± 5.59), a volunteer-based sub-cohort of theEstonian Biobank (EstBB) created in 2017 with the objective of enriching the previous existing data with microbiome
data [24, 25]. Out of the 1000 women included in this study,
two groups were established: the endometriosis group comprised of 136 patients diagnosed with this disease, and theremaining 864 individuals were grouped into the control
group. Since endometriosis has been reported to have a highdegree of comorbidity with other disorders [26–28], control
women were not diagnosed with any of the most prevalent
comorbidities of endometriosis (systemic lupus erythematosus, rheumatoid arthritis, autoimmune thyroiditis, celiac
disease, multiple sclerosis, and irritable bowel syndrome).
Endometriosis was confirmed by diagnostic laparoscopy,
and the cases were identified from the electronic healthrecord data based on the ICD-10 code (N80). Self-reported
data on diseases, medications, medical procedures, healthrelated behaviors in lifestyle, diet, physical activity, living
environment, delivery mode, and stool characteristics (Bristol stool scale) were collected for each participant [25].

Pérez‑Prieto et al. BMC Medicine(2024) 22:294

Sample collection and DNA extraction

The sample collection took place between 2017 and2019. Fresh stool samples were collected by the participants immediately after defecation with a sterile Pasteur pipette, placing the samples inside a polypropylene
conical 15-ml tube and stored in the fridge (+ 4 °C) until
transportation. The sample was subsequently delivered
to the study center where it was stored at − 80 °C until
processing.
For genomic DNA isolation, microbial DNA wasextracted using the QIAamp DNA Stool Mini Kit (Qiagen, Germany). Approximately 200 mg of stool was usedas starting material for DNA extraction following the
manufacturer’s instructions. Next, the extracted DNA
was quantified using the Qubit 2.0 Fluorometer with
dsDNA Assay Kit (Thermo Fisher Scientific). Sequencing
libraries were generated using NEBNext® Ultra™ DNA
Library Prep Kit for Illumina (NEB, USA) following the
manufacturer’s recommendations. Briefly, 1 μg DNA per
sample was used as input material, and index codes wereadded to attribute sequences to each sample. Each DNA
sample was fragmented by sonication to an average sizeof 350 bp, DNA fragments were end-polished, A-tailed,and ligated with the full-length adaptor for Illumina
sequencing with further PCR amplification. Finally, PCR
products were purified (AMPure XP system) and libraries were analyzed for size distribution by Agilent2100.
Metagenomics analyses

The shotgun metagenomic paired-end sequencing was
performed by Novogene Bioinformatics Technology
Co., Ltd. in the Illumina NovaSeq6000 platform resulting in 4.62 ± 0.44 Gb of data per sample (insert size,
350 bp; read length, 2 × 250 bp). Metagenomic analysis
was performed as previously described [25]. Briefly, thereads were trimmed for quality and adapter sequences.
The host reads that aligned to the human genome were
removed with SOAP2.21 (parameters: -s 135 -l 30 -v 7
-m 200—× 400) [29]. Quality-controlled data of eachsample was then used for metagenomic assembly using
SOAPdenovo (v. 2.04, parameters: -d 1 -M 3 -R -u –F)[30]. Next, SOAP2.21 was used to map the clean data ofeach sample to the assembled scaftigs (i.e., continuous
sequences within scaffolds). Unutilized paired-end readsof each sample were compiled together for mixed assembly. MetaGeneMark (v.3.38) was used to carry out gene
prediction (gene length > 100 bp) based on the scaftigs(≥ 500 bp), which were assembled by single and mixed
samples. CD-HIT (v.4.6) was used to dereplicate thepredicted genes based on 95% identity and 90% coverage to generate the gene catalogs (parameters: -c 0.95, -G
0, -aS 0.9, -g 1, -d 0) [31]. The longest dereplicated gene

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was defined as the representative gene (i.e., unigene).
SoapAligner [32] (v.2.21, parameters: -m 200,—× 400,
identity ≥ 95%) was then used to map the clean data to
the gene catalogs and to calculate the quantity of thegenes for each sample. The gene abundance was calculated based on the total number of the mapped reads
and the normalized gene length. The taxonomic assignment of the metagenomes was performed by comparing the marker gene homologs to a NCBI nonredundant
NCBI-nr (ftp://​ftp.​ncbi.​nlm.​nih.​gov/​blast/​db/) database(201,810) of taxonomically informative gene families
using DIAMOND (v0.9.9.110) [33]. The homologs wereannotated based on the sequence or phylogenetic similarity to the database sequences. The abundance of different taxonomic ranks was based on the gene abundancetables. As the last step, microbial functional pathways
were annotated using the Kyoto Encyclopedia of Genes
and Genomes (KEGG) (https://​www.​genome.​jp/​kegg/)
and the evolutionary gene genealogy Non-supervised
Orthologous Groups (eggNOG) database.
Microbiome analysis

Microbiome diversity analyses were performed and visualized using phyloseq [34], vegan [35], microViz [36], andggplot2 [37] packages in R. Species and KEGG Orthology groups (KOs) presented in > 10% of samples and with
0.01% or higher relative abundance were included indownstream analyses. Alpha-diversity was determinedby the Shannon diversity index and the observed number of unique species (i.e., observed richness), using the“diversity” and “specnumber” functions from the vegan
package. Case–control comparisons were tested by linear-mixed effect models (LME) to adjust for body massindex (BMI), age, frequency of antibiotics consumption
in the last year, consumption of M01A (non-steroids antiinflammatory and anti-rheumatic products), A06 (drugs
for constipation), and A02BC (proton pump inhibitors)
in the last 3 months, gut emptying frequency and stool
consistency, with the function “aov” from the stats package [38]. Beta-diversity was represented using principal
coordinate analysis (PCoA), based on the Bray Curtis dissimilarity, and tested for significance by permutational
analysis of variance (PERMANOVA) using the “adonis2”function from vegan package.
To identify the differential microbial species between
cases and controls, differential abundance analysis was
performed using an Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) from theancombc2 package [39]. ANCOM-BC models the absolute abundances using a linear regression framework [39].
Herein, absolute abundance for identified species present
in > 10% of samples with > 0.01% within each phylogenetic domain (e.g., 861 bacteria, 3 archaea, 11 eukaryota,

Pérez‑Prieto et al. BMC Medicine(2024) 22:294

and 12 viruses) was included in the differential abundance analysis. Three taxa were unclassified at the kingdom level and removed from the analysis. Additionally,
ANCOM-BC was used to examine differential KOs and
eggNOG orthologs between women with endometriosis
and controls.
PAM clustering

Fecal samples were clustered by applying the Partitioning
Around Medoids (PAM) algorithm, also simply referred
to as k-medoids, using the “pam” function from thecluster package [40]. K-medoids consists in partitioning(clustering) the data into k clusters “around medoids,”
a more robust version of K-means [41]. The number ofclusters that best fits the data was selected by looking
at the highest Silhouette Index, since 1 denotes the best
meaning that the data point is very compact within thecluster to which it belongs and far away from the otherclusters.
Estrobolome‑associated sequence reads analysis

A representative set of enzymes associated with estrogen metabolism was extracted from the atlas of humangastrointestinal microbiome-encoded enzymes fromthe Human Microbiome Project database [42]. This
atlas identified 279 unique microbial β-glucuronidase
enzymes, and revealed a functional differentiation
based on the processing of distinct substrates [42].
Those enzymes that presented an accessible ID number
were included in our study and their protein sequenceswere downloaded from the NCBI protein database [43].
Metagenomic reads were mapped to enzyme sequencesusing DIAMOND [33] software package with –mid-sensitive mode enabled. Alignments (reads) with < 90% querycoverage were filtered out. The total number of aligned
read pairs was finally reported for each enzyme involvedin the analysis. To study potential alterations in these
estrogen pathway-related enzymes in cases and controls,comparisons were performed using the ANOVA-Like
Differential Expression tool (ALDEx2 v.1.28.1) [44].
Statistical analyses

Descriptive characteristics of the study participants werereported as median (q1; q3) or frequency, as appropriate. BMI, age, frequency of antibiotics consumption in
the last year, M01A, A06, and A02BC consumption in thelast 3 months, gut emptying frequency, and stool characteristics (Bristol stool scale) were included as potential
confounders in our analyses. Five women did not record
data for age, 9 for antibiotics, 2 for gut emptying frequency, and 19 for stool consistency. Hence, we imputed
missed data using multiple imputation method in SPSS
v.28.0.1.0. For comparing non-parametric continuous

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data, Mann–Whitney U test was performed, while categorical data was analyzed by χ2 test.
Since alterations in the gut microbiome have been
widely associated with specific menopausal symptoms[21], a sensitivity analysis excluding those women withage 50 or higher was conducted to corroborate ourresults (n = 591).
All statistical analyses were performed in R (v.4.2.1)
under RStudio (v.2022.07). Statistical significance was set
to 0.05 for all analyses (i.e., p-value or q-value < 0.05 foranalyses using Benjamini–Hochberg false discovery rate(FDR) for multiple correction).

Results
Our study population of 1000 women consisted of a
total of 136 women with endometriosis and 864 control
women. The descriptive characteristics of study participants are summarized in Table 1. Study groups significantly differed for age at sample collection, M01A, A06,
and A02BC consumption, being significantly higher inwomen with endometriosis compared to controls (FDRp-values < 0.05).
Microbial landscape of the study cohort

The microbiome composition and functionality of the Estonian study population were characterized by metagenomics shotgun sequencing as previously described [25, 45].
KEGG orthology (KO) refers to a classification system used
to assign orthologous gene groups to organisms. Orthologs
are genes in different species that evolved from a common
ancestral gene. KO provides a way to organize and compare
biological information across different organisms based onthese orthologous groups, aiding in the understanding offunctional similarities and differences in molecular pathways and biological processes [46, 47].
A total of 17,158 species and 7,869 KOs were detected,
with an average of 6,942,273 species reads and 4,913,880
KOs reads per sample. After filtering by a prevalence > 10% and relative abundance > 0.01%, we identified
890 species and 1629 KOs. The average relative abundanceof bacteria was 98.14%, followed by 0.93% for taxa of viral
origin, 0.66% for eukaryotic taxa, 0.15% for archaea, and
0.13% for unclassified taxa. The most predominant phyla
were Bacteroidetes (45.15%) and Firmicutes (39.86%), followed by Proteobacteria (7.07%), Actinobacteria (1.53%),
and Verrucomicrobia (0.82%), among others (Fig. 1A).
The most abundant genera consisted of Bacteroides,
Prevotella, Clostridium, Alistipes, and Faecalibacterium(Fig. 1B). More specifically, 890 species presented > 10%
prevalence and > 0.01% of relative abundance, being
Prevotella copri, Bacteroides vulgatus, Faecalibacteriumprausnitzii, Bacteroides plebeius, and Alistipes putredinis
the most abundant microbes (Fig. 1C).

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Table 1 Descriptive characteristics of the study participants
Characteristics

Endometriosis
N = 136

Control
N = 864p-value

Age, median [q1; q3]

50.0 [40.8; 57.9]

45.0[36.0; 54.0]

0.005

BMI, median [q1; q3]

25.1 [22.2; 29.5]

24.2 [21.6; 28.6] 0.367

Frequency of antibiotics consumption, n (%)
Not in the last year

79 (58.1%)

555 (64.2%)

In the last year

26 (19.1%)

139 (16.1%)

In the last 6 months

23 (16.9%)

128 (14.8%)

In the last month

7 (5.2%)33 (3.8%)

1 (0.7%)9 (1.0%)In the last week

0.887

Microbial diversity analysis

M01A consumption in the last 3 months, n (%)
Yes

28 (20.6%)

74 (8.6%)

No

108 (79.4%)

790 (91.4%)

0.0003

A06 consumption in the last 3 months, n (%)
Yes

3 (2.2%)1 (0.1%)No133 (97.8%)

863 (99.9%)

0.022

A02BC consumption in the last 3 months, n (%)
Yes

18 (13.2%)

No

48 (5.6%)

118 (86.8%)

0.005

816 (94.4%)

G02BC consumption in the last 3 months, n (%)
Yes

2 (1.5%)14 (1.6%)

No

134 (98.5%)

850 (98.4%)

1

G03A consumption in the last 3 months, n (%)
Yes

7 (5.1%)48 (5.6%)No

129 (94.9%)

816 (94.4%)

1

Gut emptying frequency, n (%)
More than 2 times a day 21 (15.4%)

135 (15.6%)

Once a day

76 (55.9%)495 (57.3%)

3–6 times a week

29 (21.3%)

168 (19.4%)

2 times a week

3 (2.2%)

12 (1.4%)

1–2 times a week

1 (0.7%)6 (0.7%)Less than once a week

0 (0.0%)2 (0.2%)

Irregular

6 (4.4%)46 (5.3%)

0.940

12 (8.8%)

63 (7.3%)

2

31 (22.8%)

138 (16.0%)

3

22 (16.2%)

146 (16.9%)

430 (22.1%)

241 (27.9%)

5

12 (8.8%)

114 (13.2%)

6

28 (20.6%)

147 (17.0%)

71 (0.7%)15 (1.7%)

Yes

48 (35.3%)

369 (42.7%)

No

88 (64.7%)

495 (57.3%)

Next, we aimed to compare the microbial alpha- (characterized by the Shannon diversity index and observed
richness) and beta-diversity between women withand without endometriosis. No significant differences
between cases and controls were detected in alpha-diversity parameters, indicating that species richness was similar between both groups (all p-values > 0.05; Fig. 3A, B).
Beta-diversity analyses on the microbial and functional
profile (species and KOs profile) indicated no significant
dissimilarity between the groups (PERMANOVA, both
R2 < 0.0007, p-values > 0.05; Fig. 3C, D). Interestingly, thestrongest associations with beta-diversity both with species and KOs (all p-values < 0.007), were observed for thestool consistency (evaluated by the Bristol stool scale,both R2 > 0.03), gut emptying frequency (both R2 > 0.01),
antibiotics frequency (both R2 > 0.008), age (bothR2 = 0.004), and BMI (both R2 > 0.003).
Differential abundance analysis of microbial species
and KOs

Stool consistency (Bristol scale), n (%)
1

PAM clustering stratified the study population into twoenterotypes (Additional file 1: Fig. S1), where P. copri and
Bacteroides spp. drove the most significant differences in
the gut microbiome (Fig. 2A, B, Additional file 1: Fig. S2).
72% of the samples were within the Bacteroides spp. enterotype and the remaining 28% belonged to the P. copri
enterotype. The identified enterotypes were not correlatedwith the presence/absence of endometriosis, although theypresented a negative correlation with BMI and positive
with stool consistency (Fig. 2C; Additional file 2: Table S1).

0.367

Parity, n (%)
0.249

Data presented as median [q1, q3] and frequency, as appropriate. P-values
adjusted by Benjamini–Hochberg false discovery rate (FDR). Abbreviations:
BMI, body mass index; M01A, anti-inflammatory and anti-rheumatic products,
non-steroids; A06, drugs for constipation; A02BC, proton pump inhibitors; G02B,
contraceptives for topical use; G03A, hormonal contraceptives for systemic use

To detect specific species or microbial pathways
that could be potentially involved in the pathogenesis of the disease, an ANCOM-BC analysis was
performed on the identified species and KOs. Overall, 28 bacteria seemed to be differentially abundant between groups, for example, Clostridium sp.
CAG:307 (logFC = 0.595, p = 0.019) and Acinetobacter
sp. CAG:196 (logFC = 0.745, p = 0.017) were enrichedin the endometriosis group, whereas Ruminococcus
sp. CAG:177 (logFC =  − 0.420, p = 0.023) and Roseburia sp. CAG:45 (logFC =  − 0.369, p = 0.005) weredecreased compared to controls (Additional file 2:
Table S2). Regarding functional analysis, 12 KOs
associated with endometriosis, including nitrogenmetabolism (logFC =  − 0.176, p = 0.016) or oxidative phosphorylation (logFC =  − 0.040, p = 0.025) thatwere decreased, while 8 KOs including fatty acid biosynthesis (logFC = 0.145, p = 0.035) and amino acids

Pérez‑Prieto et al. BMC Medicine(2024) 22:294

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Fig. 1 Microbial landscape in the Estonian study population. Circular stacked barplots (“iris plots”) show the most relatively abundant phyla (A),genera (B), and species (C) in the study population. The outer bicolor rings indicate the endometriosis and control groupsPérez‑Prieto et al. BMC Medicine(2024) 22:294

Page 7 of 12

Fig. 2 Enterotypes identified in the Estonian study population. A, B Relative abundance of Prevotella copri and Bacteroides spp.
within the enterotypes on the principal coordinates analysis (PCoA) plot of the species-level microbiome profile based on the Bray–Curtis
dissimilarity. C Distribution of women with and without endometriosis within the enterotypes. The dot’s shape indicates the cluster, while the colors
highlight the relative abundances (A, B) or the endometriosis and control groups (C)Fig. 3 Microbial diversity measures in endometriosis and control groups. A, B Alpha-diversity analysis (i.e., Shannon diversity index and observed
richness). Groups comparisons indicate no significant differences (linear-mixed effects, all p-values > 0.05). C, D Beta-diversity analyses
on the principal coordinates analysis (PCoA) of the species (C) and KOs (D) profile based on the Bray–Curtis dissimilarity (Adonis PERMANOVA,
both R.2 < 0.0007, both p-values > 0.05)

metabolism (logFC = 0.049, p = 0.016) were increased
in women with endometriosis compared to controls(Fig. 4). However, no bacteria and KOs remained significantly different after FDR correction (all p-values > 0.05) (Additional file 2: Tables S2–S3). The
functional analysis’ results are supported by the comparison of eggNOG orthologs, which did not reveal
significant differences between the study groups (allFDR p-values > 0.05) (Additional file 2: Table S4).

Sensitivity analysis

A sensitivity analysis including only women at theirreproductive age (≤ 50 years) and excluding women atmenopause (> 50 years) was performed to corroborate
the previous results on the whole cohort. A total of 66
women with endometriosis and 525 control women were
finally included. The obtained results were similar to thewhole cohort results, detecting no statistically significant
differences between the groups in microbial diversity

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Fig. 4 Functional differences in the microbial pathways in endometriosis and control groups. Volcano plot displaying log fold change differencesin the KEGG orthologs derived from the ANCOM-BC model. Points in blue and red represent KEGG orthologs which were decreased and increasedin endometriosis and statistically significant (p < 0.05). Points in black represent KEGG orthologs that were not significantly different (p > 0.05). No
KEGG orthologs remained statistically significant after Benjamini–Hochberg false discovery rate (FDR) correction (all FDR p-values > 0.05)

and differential abundance analyses on the species andKOs profiles (Additional files 1 and 2: Fig. S3 and Tables
S5–S6).
Estrobolome pathway analysis

Since estrogen metabolism has been described as a keystone factor in the pathogenesis of proliferative disorders such as endometriosis, we analyzed key enzymes
from the estrobolome that could lead to hyperestrogenic
conditions. Thus, we compared the total read count of
156 estrogen pathway-related enzymes (including betaglucuronidases and beta-galactosidases) between thewomen with and without endometriosis. No significant
differences were detected in the total read count between
the cases and controls (p > 0.05, Additional file 1: Fig.
S4). Additionally, each enzyme was compared between
groups using the ALDEx2 package (v1.28.1). We did notobserve any enzyme with statistically significant differences between the endometriosis and control women (allp-values > 0.05, Additional file 2: Table S7). Multiple testing correction was applied for all analyses.

Discussion
Endometriosis is a widespread gynecological disorder,
and despite active research, there is a lack of understanding of the pathogenesis of the disease and its associated symptoms. Scientific evidence supports that
estrogen drives the proliferation of endometrial-likelesions, although the reason why some women develop
endometriosis and others do not remains unclear. Since

the role of the gut microbiome in inflammatory andproliferative conditions as well as in estrogen metabolism is established [18, 21], it is rational to propose an
involvement of the gut microbiome in the developmentof the disease. Indeed, novel studies are focusing on thegut microbial communities as important candidates forinvestigation in reproductive health, and several studies are associating uterine microbes with endometriosis[48–51].
To the best of our knowledge, our study is the firstwhole metagenome study (identifying bacteria, viruses,
fungi, protozoa, and archaea) performed in women withendometriosis, while all previous studies have exclusively
analyzed the 16S rRNA gene region of the bacteria. Our
study results did not identify distinct compositional orfunctional gut microbial profiles in women with endometriosis compared to controls, which has been observed
also in a previous marker gene-based study (16S rRNA
gene analysis) [52]. However, other marker gene-based
studies have associated several gut microbes with endometriosis [53, 54]. The largest study conducted up todate analyzed the gut microbiome profile of 66 womenwith endometriosis and 198 control women [53], wherea higher abundance of Parabacteroides genus and lower
Paraprevotella in endometriosis patients compared
to controls were detected. In our study of 1000 participants, we detected a decrease in Paraprevotella clara
and Parabacteroides sp. D26 in women with endometriosis, although these differences disappeared after multiple testing correction. A recent study compared thePérez‑Prieto et al. BMC Medicine(2024) 22:294

gut microbiome in 12 patients with moderate-to-severe
endometriosis and 12 healthy women [54]. Although
they did not describe any statistically significant differences in alpha-diversity, several genera such as Blautia,
Bifidobacterium, Dorea, and Streptococcus, were significantly increased in the endometriosis group compared
to controls, while Lachnospira and Eubacterium eligens
group showed a decreased abundance in women withendometriosis. Another study built classification models
with machine learning on the vaginal and gut microbial
composition to predict rASRM stages 1–2 (minimal-tomedium) vs. 3–4 (moderate-to-severe) endometriosis
and found that the microbe that contributes the mostto this prediction was Anaerococcus genus [55]. In ourstudy, species from the Anaerococcus genus, however,were not detected. Nonetheless, current studies are
hardly comparable due to the different sample sizes andmicrobiome detection methods, proving contradicting
and inconclusive results. Importantly, contrastingly to
our study where we analyzed species level by shotgun
sequencing, the previous studies performed a 16S rRNA
gene analysis, which limits a reliable taxonomic assignment to genus level.
Recently, a higher frequency of Fusobacterium in boththe endometria and ovarian endometriotic tissues from
79 patients with endometriosis was detected when compared to endometria from 76 control women [56]. Hence,
they investigated further the pathogenic role of this bacteria in the development of endometriosis. Interestingly,
we detected a higher relative abundance of Fusobacterium sp. CAG:815 in the gut in women with endometriosis, although the differences did not remain significant
after adjustment for multiple comparisons.
While evidence supporting the role of the endometrial transcriptome in endometriosis development isaccumulating [57, 58], a new debate is whether there
are microbial pathways involved in the pathogenesis of
the disease. In this context, our study identified several
KOs possibly associated with the presence of endometriosis. We noted that a KO related to the long-chain
saturated fatty acids biosynthesis, a metabolic pathway
catalyzed by fatty acid synthase (FASN) was increased
in women with endometriosis. In some cancer celllines, based on the host gene homologs, FASN has
been found to be fused with estrogen receptor, and its
overexpression is a common molecular feature in hormone-sensitive cells, being regulated by both estradiol
and progesterone [59]. During the menstrual cycle,
FASN expression appears to be linked to endometrial
cell proliferation [60, 61]. Thus, inhibiting FASN has
been proposed as a therapy targeting estrogen receptor signaling in breast and endometrial cancer [62].
In fact, several studies associate the high prevalence

Page 9 of 12

of endometriosis with excessive lipid intake or a lipid
intake imbalance and propose novel lipid metabolismtargeted approaches for the treatment of endometriosis
due to the proliferative and inflammatory state of thedisease [63].
We also explored the microbial genes involved in
estrogen metabolism, the estrobolome, that is recognized as an important factor in the development ofproliferative disorders, including endometriosis [18,64]. Through a comprehensive analysis of 156 estrogen
pathway-related enzymes, including main candidates
like beta-glucuronidases and beta-galactosidases, nosignificant differences in any of these enzymes between
the case–control groups were detected. In line with
our study results, a recent study conducted enzymatic
activity assays on fecal samples from women with andwithout endometriosis and detected no significant differences in the average level of β-glucuronidase activity between groups [64]. These findings suggest thatalterations in the abundance of these specific enzymes
from the estrobolome may not directly correlate withthe presence of endometriosis in our studied cohort.
Nevertheless, the estrogen-estrobolome-endometriosis
axis is complex and our study results cannot rule out
its importance in the disease development, which warrants further research.
Our study provides pioneering results about the gut
microbiome composition and association with endometriosis on a large-scale study population, however, it
has several limitations that should be highlighted. First,
the detection power in our case–control study might
have been influenced by including different subtypes ofendometriosis. Endometriosis is defined as a heterogeneous disease broadly characterized into three phenotypes with different grades of severity: from superficial
peritoneal as the least severe form, to ovarian and deep
infiltrating endometriosis, the last being the most severe
phenotype [4]. Since the inclusion of the three phenotypes could mask the presence of microbial alterationsin the most severe forms, additional analyses on the different subtypes are needed to confirm our results. Furthermore, hormonal imbalance has been demonstrated
to have a negative impact on the gut microbiome, while
it has been reported that hormonal treatment reverses
the gut microbiome dysbiosis in reproductive disorders[65]. Since the use of estrogen-progestins and progestinsis the first-line medical treatment in endometriosis [19],patients with hormonal treatment may present similar
gut microbial profiles than those without the disease.
Hence, more studies on women with active endometriosis and no hormonal treatment are warranted to unravel
the complex bidirectional relationship between the gut
microbiome and endometriosis.

Pérez‑Prieto et al. BMC Medicine(2024) 22:294

Conclusions
The molecular mechanisms underlying the pathogenesisof endometriosis are not yet fully understood, which presents a challenge in its diagnosis and treatment. In thiscontext, the gut microbiome emerges as a potential diagnostic tool and therapeutic target. We present the largest
whole metagenome study on endometriosis so far; however, our study findings do not provide enough evidence
to support the existence of a gut microbiome-dependent
mechanism implicated in the pathogenesis of endometriosis. Further research, especially involving large-scale
study populations with active endometriosis and without
hormonal treatment, is crucial to better understand theendometriosis-associated microbiome, the microbiomeimmune response axis, and to unravel its potential for
diagnosis and treatment approaches.
Abbreviations
ABC	ATP-binding cassette
ANCOM-BC	Analysis of Compositions of Microbiomes with Bias Correction
BMI	Body mass index
FASN	Fatty acid synthase
KEGG	Kyoto Encyclopedia of Genes and Genomes
KO	KEGG Orthology groupLME	Linear-mixed effect models
PAM	Partitioning Around Medoids
PCoA	Principal coordinates analysis
PERMANOVA	Permutational analysis of variance

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03503-y.
Additional file 1: Figures S1-S4. FigS1- Enterotypes model fit by the
number of clusters. 2 clusters were selected as an optimal number basedon the highest Silhouette Index. FigS2- Heatmap illustrating the top 20
most abundant species of the Estonian study population. The taxa arerank-ordered with the most abundant taxon on the left in the x-axis. Par‑
ticipants are displayed in the y-axis. FigS3- Sensitivity analysis of microbial
diversity measures in endometriosis and control groups. (A, B) Alphadiversity analysis (i.e., Shannon diversity index and observed richness)after excluding women with age > 50. Groups comparisons indicate no
significant differences (Linear-mixed effects, all p-values > 0.05). (C, D) Betadiversity analyses on the principal coordinates analysis (PCoA) of the spe‑
cies (C) and KOs (D) profile based on the Bray–Curtis dissimilarity (Adonis
PERMANOVA, both R2 > 0.001, both p-values > 0.05). FigS4- Estrobolome
analysis comparing endometriosis and control groups. Total read countfrom estrogen-related enzymes was not significantly different between
groups (Mann Whitney U test: p-value > 0.05).
Additional file 2: Tables S1-S7. Table S1- Correlation analysis between gut
enterotypes and clinical factors. Table S2- Differential abundance analysis
in endometriosis and control groups. Species with a prevalence > 10% andrelative abundance ≥ 0.1% were compared in endometriosis and control
groups using an Analysis of Compositions of Microbiomes with Bias Cor‑
rection (ANCOM-BC). Table S3- Differential abundance analysis in endome‑
triosis and control groups. KEGG orthologs (KO) with a prevalence > 10%
and relative abundance ≥ 0.1% were compared in endometriosis andcontrol groups using an Analysis of Compositions of Microbiomes
with Bias Correction (ANCOM-BC). Table S4- Differential abundance
analysis in endometriosis and control groups. EggNOG orthologs witha prevalence > 10% and relative abundance ≥ 0.1% were compared inendometriosis and control groups using an Analysis of Compositionsof Microbiomes with Bias Correction (ANCOM-BC). Table S5- Sensitivity

Page 10 of 12

differential abundance analysis in endometriosis and control groups.
Species with a prevalence > 10% and relative abundance ≥ 0.1% werecompared in endometriosis and control groups after excluding womenwith age > 50, using an Analysis of Compositions of Microbiomes with
Bias Correction (ANCOM-BC). Table S6- Sensitivity differential abundance
analysis in endometriosis and control groups. KEGG orthologs (KO) witha prevalence > 10% and relative abundance ≥ 0.1% were compared inendometriosis and control groups after excluding women with age > 50,
using an Analysis of Compositions of Microbiomes with Bias Correction(ANCOM-BC). Table S7- Estrogen path enzymes with ALDEx2 analysis
between endometriosis and control groups.
Acknowledgements
This work is part of a Ph.D. thesis conducted in the Biomedicine Doctoral Stud‑ies of the University of Granada, Spain.
Authors’ contributions
A.S., E.O., and S.A. conceived and designed the study; I.P.P., E.V., E.S.E., K.L.,
A.C.G., L.A.P., R.A., O.A., K.W., E.O., and S.A. performed the microbiome analyses;
K.L., O.A., E.B., and E.O. participated in data generation; I.P.P., E.V., E.S.E., E.S.E.,
K.L., A.C.G., J.F., A.S., K.W., E.O., and S.A. interpreted the results. I.P.P., E.V., A.C.G.,
and S.A. drafted the manuscript. All authors reviewed the manuscript draft
for important intellectual content. All authors read and approved the final
manuscript.
Authors’ Twitter handles
Twitter handles: @InmaPerezPrieto (Inmaculada Pérez-Prieto).
Funding
This work is supported by Grants Endo-Map (PID2021-12728OB-100)
and ROSY (CNS2022-135999) funded by MICIU/AEI/https://doi.
org/10.13039/501100011033and by FEDER, EU; Estonian Research Council
grants (grants No. PRG1414 to EO and PRG1076); EMBO Installation grant (No.
3573 to EO); Estonian Center of Genomics/ Roadmap II (project No. 16–0125);
Horizon 2020 innovation grant (ERIN, grant No. EU952516); Grant FPU19/05561
funded by MCIN/AEI/https://doi.org/10.13039/501100011033and by ESF
Investing in your future; Plan de Recuperación, Transformación y resiliencia,
Ayudas para la recualificación del sistema universitario español, Ayudas Mar‑garita Salas (ref. UJAR01MS).
Availability of data and materials
The metagenomic data analyzed during the current study are available in theEuropean Genome-Phenome Archive database (https://​www.​ebi.​ac.​uk/​ega/)
under accession code EGAS00001008448.

Declarations
Ethics approval and consent to participate
All participants included in the EstMB provided informed consent for thedata and samples to be used for scientific purposes. This study was approvedby the Research Ethics Committee of the University of Tartu (approval No.
266/ T10) and by the Estonian Committee on Bioethics and Human Research(Estonian Ministry of Social Affairs; approval No. 1.1–12/17).
Consent for publication
Not applicable.
Competing interests
The authors report there are no competing interests to declare.
Author details
1
Department of Biochemistry and Molecular Biology I, Faculty of Sciences,
University of Granada, Granada, Spain. 2 Instituto de Investigación Biosanitaria
Ibs.GRANADA, Granada, Spain. 3 Systems Biology Unit, Department of Experi‑mental Biology, Faculty of Experimental Sciences, University of Jaen, Jaen,
Spain. 4 Institute of Genomics, Estonian Genome Centre, University of Tartu,
Tartu, Estonia. 5 Department of Physiology, Faculty of Veterinary, Universityof Murcia, IMIB-Arrixaca, Campus Mare Nostrum, Murcia, Spain. 6 Department

Pérez‑Prieto et al. BMC Medicine(2024) 22:294

of Computer Engineering, Automation and Robotics, University of Granada,
Granada, Spain. 7 U. Reproducción, UGC Laboratorio Clínico y UGC Obstetricia
y Ginecología. HU Virgen de Las Nieves, Granada, Spain. 8 Competence Centre
On Health Technologies, Tartu, Estonia. 9 Division of Obstetrics and Gynecol‑ogy, Department of Clinical Science, Intervention and Technology, Karolinska
Institutet and Karolinska University Hospital, Stockholm, Sweden. 10 Depart‑
ment of Obstetrics and Gynaecology, Institute of Clinical Medicine, Universityof Tartu, Tartu, Estonia. 11 Institute of Molecular and Cell Biology, Universityof Tartu, Tartu, Estonia. 12 Institute of Genomics, Estonian Genome Center,
University of Tartu, Tartu, Estonia. 13 School of Biological Sciences, Universityof California, Irvine, CA, USA.
Received: 20 January 2024 Accepted: 26 June 2024

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